Automatic image segmentation is a well-recognized problem medical imaging applications that is being addressed in many different ways. Atlas-based segmentation is one known solution which treats segmentation as a registration problem by elastically matching a pre-segmented atlas to the target image. Examples of such atlas-based segmentations are described by D. L. Collins, C. J. Holes, T. M. Peters, and A. C. Evans, Automatic 3-D model-based Neuroanatomical Segmentation, HUMAN BRAIN MAPPING, 3:190-208, 1995; S. L. Hartmann, M. H. Parks, P. R. Martin, and B. M. Dawant, Automatic 3-D Segmentation of Internal Structures of the Head in MR Images Using a Combination of Similarity and Free-Form Transformations: Part II, Validation on Severely Atrophied Brains, IEEE T. MI, 18(10:917-926, 1999; and R. Kilkinins, M. E. Shenton, and et al., A Digital Brain Atlas for Surgical Planning Model-Driven Segmentation, and Teaching, IEEE T. VIS. COMP. GRAPHICS, 2(3):232-240, 1996. However, atlas-based segmentation approaches are not suited for structures that are not stable over the population.
Another known segmentation method is active shape and appearance models described by T. F. Cootes, D. Cooper, C. J. Taylor, and J. Graham, Active Shape Models—Their Training and Application, COMPUT. VIS. IMAGE UND., 61(1):38-59, 1995; T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active Appearance Models, IEEE T. PAMI, 23(6):681-685, 2001; and A. Kelemen, G. Székely, and G. Gerig, Elastic Model-based Segmentation of 3-D Neuroradiological Data Sets, IEEE T. MI, 18(10):828-839, 1999. These methods assume a statistical correlation between the shape or appearance of the organs over the population which may not be the most accurate assumption for a given individual case.
Thus, there is a continuing need for an improved method of automatically segmenting images in medical imaging applications.